Distributed object tracking for augmented reality application

ABSTRACT

One embodiment of the present invention provides a system for tracking and distributing annotations for a video stream. During operation, the system receives, at an annotation server, the video stream originating from a remote field computer, extracts a number of features from the received video stream, and identifies a group of features that matches a known feature group, which is associated with an annotation. The system further associates the identified group of features with the annotation, and forwards the identified group of features and the annotation to the remote field computer, thereby facilitating the remote field computer to associate the annotation with a group of locally extracted features and display the video stream with the annotation placed in a location based at least on locations of the locally extracted features.

BACKGROUND

1. Field

This disclosure is generally related to a system for generatingaugmented reality. More specifically, this disclosure is related to adistributed system for generating augmented reality.

2. Related Art

Augmented reality (AR) enhances a user's perception of the real world bysuperimposing computer-generated sensory input, such as sound, video,graphics, or GPS data, over a real-world environment in real time.Current augmented reality solutions generally run on a singlemachine/device. More specifically, a camera mounted on the deviceperceives scenes of the real-world environment and transmits its outputto a tracking module (of the device), which identifies features in thereal-world images and then aligns a plurality of computer-generatedobjects in 3D space with the scenes being tracked, based on an analysisof the relevant positions of the features in the real-world images,sometimes assisted by a 3D model. Finally, a rendering engine (on thedevice) renders the scenes with the objects in thepositions/orientations determined by the tracking module on top of thereal-world images, generating the appearance of virtual objectsintegrated with the real-world scenes. However, the increased prevalenceof networked personal video cameras (often associated with smartphones)means that the single machine is not appropriate in all situations.

SUMMARY

One embodiment of the present invention provides a system for trackingand distributing annotations for a video stream. During operation, thesystem receives, at an annotation server, the video stream originatingfrom a remote field computer, extracts a number of features from thereceived video stream, and identifies a group of features that matches aknown feature group, which is associated with an annotation. The systemfurther associates the identified group of features with the annotation,and forwards the identified group of features and the annotation to theremote field computer, thereby facilitating the remote field computer toassociate the annotation with a group of locally extracted features anddisplay the video stream with the annotation placed in a location basedat least on locations of the locally extracted features.

In a variation on this embodiment, the system further receives, from aclient computer, a video frame, which includes annotations added by auser of the client computer, extracted from the video stream, andattaches the annotations to corresponding features within the videoframe based on one or more user-definable rules associated with theannotations.

In a further variation, the system receives, from the user, updates tothe annotations, and reattaches the updated annotations to featureswithin the video frame.

In a further variation, the system receives, from the user, updates tothe user-definable rules, and reattaches the annotations to featureswithin the video frame based on the updated user-definable rules.

In a further variation, the system stores the corresponding features andthe attached annotations as known features in a database.

In a variation on this embodiment, the system tracks the identifiedgroup of features frame by frame in the received video stream.

In a variation on this embodiment, the system receives the video streamfrom a video reflector, which reflects the video stream originating froma remote field computer to the annotation server and at least one otherclient computer.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 presents a diagram illustrating an exemplary distributedobject-tracking system for augmented reality, in accordance with anembodiment of the present invention.

FIG. 2 presents a diagram illustrating the architecture of an exemplaryaugmented reality server, in accordance with an embodiment of thepresent invention.

FIG. 3 presents a diagram illustrating the architecture of an exemplaryfield client, in accordance with an embodiment of the present invention.

FIG. 4 presents a time-space diagram illustrating the distributedobject-tracking process, in accordance with an embodiment of the presentinvention.

FIG. 5 presents a flowchart illustrating an exemplary server process forannotation mapping and distribution, in accordance with an embodiment ofthe present invention.

FIG. 6 illustrates an exemplary computer system for distributed objecttracking, in accordance with one embodiment of the present invention.

In the figures, like reference numerals refer to the same figureelements.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the embodiments, and is provided in the contextof a particular application and its requirements. Various modificationsto the disclosed embodiments will be readily apparent to those skilledin the art, and the general principles defined herein may be applied toother embodiments and applications without departing from the spirit andscope of the present disclosure. Thus, the present invention is notlimited to the embodiments shown, but is to be accorded the widest scopeconsistent with the principles and features disclosed herein.

Overview

Embodiments of the present invention provide a distributed system forproviding augmented realities. The system includes a field client, areflector, an augmented reality (AR) server, and a number of webclients. During operation, a camera mounted on the field client shootsvideos of the real-world environment. The videos are streamed live fromthe field client to a video reflector. The video reflector receives thelive video stream, and forwards the video stream to the AR server andthe web clients. Note that the reflector and AR server can be embodiedin the same server implementation. The AR server also includes a featuredetector capable of matching the incoming video stream with a list offeatures (such as edges, corners, shapes, etc.) that have been connectedto or associated with annotations. The AR server is also responsible fordetermining the positions of the annotations, receiving annotationalterations from the web clients, and communicating changes to theannotations to all clients.

Distributed Object-Tracking System

One key challenge for creating an augmented reality is to maintainaccurate registrations between the real-world scene and thecomputer-generated objects. As users move their viewpoints, thecomputer-generated graphic elements must retain their alignment with theobserved 3D positions and orientations of the objects in the real-worldscene. To do so, it is important to track the locations and orientationsof the real-world objects continuously.

As networked video cameras (such as WiFi-enabled surveillance cameras)and cameras attached to IP-enabled mobile devices (such as smartphones)become widely available, AR systems may rely on videos captured by thesecameras to provide the real-world scenes for an AR. However, the camerasor the mobile devices that the cameras are attached to may not havesufficient computational power to track the locations of theorientations of the real-world objects in the videos and to attachannotations to those objects, particularly when sophisticatedimage-analysis methods are employed. To overcome such obstacles, inembodiments of the present invention, the object-tracking andannotation-attachment functions are realized by a remote server havingsufficient computational power.

FIG. 1 presents a diagram illustrating an exemplary distributedobject-tracking system for augmented reality, in accordance with anembodiment of the present invention. Distributed object-tracking system100 includes a number of clients, such as a field client 102 and a webclient 108, a video reflector 104, and an AR server 106.

Field client 102 can be any type of computing device that is equippedwith a video camera. For example, field client 102 can include, but isnot limited to: a web-enabled surveillance camera; a conventional webcamhosted by a personal computer (PC), a laptop computer, or a tabletcomputer; and a smartphone. In one embodiment, field client 102 is ahead-mounted computer (HMC), such as a Motorola™ Golden-i head-mountedcomputer, which allows a user to operate a cursor by moving his head andaccepts voice commands. These hands-free features allow the user offield client 102 to operate the computer while engaging in certainactions, such as servicing a piece of equipment or performing a medicalprocedure.

During operation, field client 102 is responsible for capturing videosreflecting the real-world scenes (such as the equipment being servicedor a surgical scene) and streaming the videos to video reflector 104.Note that, compared with the conventional AR system where the camera andthe object-tracking module are collocated on a single machine havingsufficient computational power for feature tracking, the limitedcomputational power of field client 102 means that field client 102 canonly perform certain lightweight feature correspondence, such asextracting features, including edges, corners, shapes, etc., from thevideos. These features are expressed as vectors (using featuredescriptors) in the feature space. Instead of mapping the extractedfeatures to known annotations, field client 102 receives such mappingresults (which include the features and their associated annotations)from a remote server, such as AR server 106, and displays these featuresand annotations over the real-world videos to allow a user of fieldclient 102 to experience the augmented reality.

Video reflector 104 is responsible for receiving videos from videosenders, such as field client 102, and sending the received videos to ARserver 106 and other clients, such as web client 108. In one embodiment,video reflector 104 can also resample the received video to achieve adesired frame rate or size. Video reflector can be a standalone device,or it can reside on field client 102 or AR server 106.

Web client 108 can be an electronic device that receives video streamsfrom video reflector 104, presents the video streams to a user (such asan expert), receives annotations for the videos from a user, and sendsthe annotations to AR server 106. In one embodiment, web client 108subscribes to video sources, which can be video reflector 104 or ARserver 106, and receives the video subscriptions once they areavailable.

When viewing the streamed video, which can be a live video steam fromfield client 102, a user of web client 108 can add annotations to thevideo stream. For example, to explain to a remote trainee how to servicea piece of equipment, an expert user may draw an arrow pointed to acertain feature (such as a button or a handle bar) in a video frameshowing the piece of the equipment. Web client 108 further displaysthese annotations on top of the video (either in the form of 2D objectsor in the form of 3D pose-locked objects) to its users. In addition, webclient 108 captures interactions between the user and the annotations,and transmits the annotations including the changes to AR server 106.For example, a user may, while viewing the annotated video via webclient 108, drag an annotation (which can be expressed as a 2D object)within a frame to a new location; or he may add reference information(as an annotation or a tag) describing the frame (such as associatingcertain features with a name). The modifications to the annotation andthe reference information are sent to AR server 106 and cause thefeature-matching rules to be applied and the object to be bound to thefeatures in the frame. Consequently, AR server 106 is able to trackthese features frame by frame. Another function of web client 108 is toreceive user-defined feature-matching rules and send these user-definedrules to the rule-based feature-matching system located on AR server106.

AR server 106 performs the heavyweight image-processing tasks, includingfeature detections, on the videos. More specifically, AR server 106includes a feature-detection module that maps the features to theannotations using a rule-based system, which can reside on AR server106. During operation, AR server 106 receives annotated video framesfrom web client 108, and binds the annotations to features in thecorresponding video frames. In one embodiment, binding the annotation tofeatures is performed by the rule-based system. The rule-based systemstores the feature mapping rules, and applies these rules to theextracted features. For example, a rule for a text annotation mayrequire a “match to all the features defined by a rectangular area,” anda rule for an arrow annotation may involve “pointing the arrow at thelarge major feature found near the tip of the arrow.” Note that in oneembodiment the rule-based system continues to receive feature-mappingrules from a user who views the video via web client 108. Once theannotations are bound to the features, AR server 106 is able to trackthese features even after their positions and orientations have changedin later frames. For example, once an arrow is bound to a feature, suchas a button on a piece of equipment, even if the button is shown at adifferent location in later received video frames, AR server 106 cancorrectly align that arrow to the button in those later received videoframes. The binding between the features and the annotations can bestored in the rule-based system.

In the meantime, AR server 106 concurrently streams videos from videoreflector 104, extracts features from the incoming videos, and maps theextracted features against a list of known features (which have beenpreviously bounded or associated with annotations). Note that theannotations can be the name of a feature or additional informationassociated with the feature.

AR server 106 may receive real-time annotation updates via interactionswith a client (either field client 102 or web client 108). For example,while viewing the augmented reality, a user may want to change theannotation of a feature. The updating of the annotations triggers therule-based feature-matching system to determine matching features of theupdated annotations. In one embodiment, in order to maintain coherenceto changes made to the features at the clients, AR server 106 maintainsa short history of frames of the video. Note that, in general,consecutive frames have a large number of features in common. Aftermapping, the features (as a vector) along with the mapped annotationsare sent back to the clients, including field client 102 and web client108. Each client then displays the annotations along with the featuresin the real-world scene accordingly to allow a user of the client toexperience augmented reality.

In some embodiments, field client 102 and web client 108 are clientcomputers that interact with AR server 106 in a client-serverarchitecture. A given client computer may include one or more softwareapplications that are resident on and which execute on the given clientcomputer. A particular software application may be a standalone programor may be embedded in another software application. Alternatively, thesoftware application may be a software-application tool that is embeddedin a web page (e.g., the software application may execute in anenvironment provided by a web browser).

FIG. 2 presents a diagram illustrating the architecture of an exemplaryaugmented reality server, in accordance with an embodiment of thepresent invention. AR server 200 includes a video-streaming module 202,a feature-extraction module 204, an annotation-receiving module 206, anannotation-attachment module 208, a rule database 210, a feature-mappingmodule 212, and an annotation distributor 214.

Video-streaming module 202 is responsible for streaming videosreflecting real-world scenes from a video reflector, which receives thevideos from a remote video source, such as a field client. The videostreams are sent to feature-extraction module 204, which extractsfeatures of the real-world scenes from the video streams.

Annotation-receiving module 206 receives annotations authored by a user(often an expert) from a web client. Note that the web clientconcurrently streams the real-world videos from the video reflector, andthe user of the web client views the video and adds annotations on topof one or more video frames. In one embodiment, the annotations mayinclude 2D or 3D objects. Examples of annotations may include, but arenot limited to: text descriptions, audio clips, arrows, circles, boxes,and other shapes. The received annotations are sent toannotation-attachment module 208, which is responsible for attaching (orlocking) the annotations to corresponding features using rules stored inrule database 210. The results of the annotation attachment (whichdescribes which annotation is attached to what features) are stored inrule database 210. Note that the annotations and the rules can beupdated by the expert user, and each annotation update results in therules being applied to determine features that match the updatedannotations.

Once the annotations have been attached to corresponding features, theseannotations can be correctly aligned to the same features in thesubsequently received video frames, even after the features have changedtheir locations and orientations in the subsequent frames. This isachieved by feature-mapping module 212. More specifically,feature-mapping module 212 receives extracted features fromfeature-extraction module 204, and determines which features match theannotations based on the previously determined annotation-featureattachment results stored in rule database 210. In one embodiment,feature-mapping module 212 uses a previously constructed 3D model totrack features frame by frame.

Outcomes of the feature mapping are sent to annotation distributor 214,which distributes the annotations along with the attached features tothe clients, including the field client and the web client. Note thateach client also maintains a list of locally extracted features. Hence,by comparing the local list with the annotation-feature-mapping resultsent by annotation distributor 214, each client is able to associateannotations with the local features, and display the annotations on topof the real-world scenes with the annotations correctly aligned to thecorresponding features.

FIG. 3 presents a diagram illustrating the architecture of an exemplaryfield client, in accordance with an embodiment of the present invention.Field client 300 includes a camera 302, a video-streaming module 304, afeature-extraction module 306, an annotation-receiving module 308, and adisplay 310.

During operation, camera 302 records real-world video scenes, andvideo-streaming module 304 streams the videos to a video reflector,which in turn forwards the videos to an AR server and a web client.Feature-extraction module 306 extracts features from the videosAnnotation-receiving module 308 receives annotations and theannotation-feature-mapping result from the AR server. Display 310displays the received annotations on top of the videos based on theextracted features and the annotation-feature-mapping result, thusenabling a user of field client 300 to experience the augmented reality.

FIG. 4 presents a time-space diagram illustrating the distributedobject-tracking process, in accordance with an embodiment of the presentinvention. During operation, a field client 402 captures video framesreflecting the real-world scenes (operation 410), and concurrentlystreams the captured video frames to a video reflector 404 (operation412). Field client 402 can be a head-mounted computer, a webcam hostedby a PC, a mobile computing device equipped with a camera, or aweb-enabled wired or wireless surveillance camera. Field client 402 alsoperforms certain lightweight image-processing operations, such asextracting features from the video frames (operation 414). In oneembodiment, field client 402 also detects the orientation of the camerarelative to an object within a video frame.

Video reflector 404 forwards the video streams to AR server 406 and webclient 408 (operations 416 and 418, respectively).

AR server 406 extracts features from the received videos (operation420), and performs feature detection, which involves extracting featuresand mapping the features to available annotations, on the video streams(operation 422). In one embodiment, a rule-based system is used to mapfeatures to annotations. The rule-based system resides on AR server 406and stores a list of features that have been previously associated withannotations.

A user of web client 408 views videos along with the annotations andupdates/modifies the annotations (operation 424). For example, anannotation object may be dragged to a new location or new annotationsmay be added. Web client 408 subsequently sends the annotation update toAR server 406 (operation 426). The annotation update triggers AR server406 to reapply annotation-feature-attachment rules and update themapping between the features and annotations (operation 428). AR server406 then distributes the annotations along with the annotation-featuremapping to field client 402 and web client 408 (operations 430 and 432,respectively). Field client 402 and web client 408 then display thevideos with annotations correctly aligned with their matching features(operations 434 and 436, respectively).

FIG. 5 presents a flowchart illustrating an exemplary server process forannotation mapping and distribution, in accordance with an embodiment ofthe present invention. During operation, the annotation or AR serverreceives a video stream (operation 502). In one embodiment, the videostream is received from a video reflector, which reflects videosoriginating from a remote field camera. The annotation server extractsfeatures from the received video stream (operation 504), and comparesthe extracted features with a list of known features (operation 506).The known features are features that have been associated with availableannotations previously. Based on the comparison, the annotation serverdetermines whether there are any matching features corresponding to theavailable annotations (operation 508). Note that, if the list of knownfeatures is empty, then no matching features can be found. If there arematching features corresponding to the available annotations, theannotation server identifies those matching features (operation 510),and associates these matching features with the available annotations(operation 512). The associations between the annotations and featuresare then sent to the clients, including the field client and a web-basedclient (operation 514).

If there are no matching features corresponding to the availableannotations, the annotation server waits to receive annotated videoframes from an expert user via a web-based client (operation 516). Oncein receipt of the annotated video frame, the annotation serveridentifies the user-defined annotation objects (such as text descriptionor arrows) and attaches these annotation objects to correspondingfeatures in the videos (operation 518). In one embodiment, whileattaching the annotations to corresponding features, the annotationserver applies a set of rules, which can be system default or defined bythe user). The features and the attached annotations are stored in thelist of known features (operation 520), and the associations are sent toa number of clients (operation 514).

Note that, compared with conventional AR technologies where the videocapturing, feature tracking, and feature-to-annotation mapping areperformed by a same machine, embodiments of the present invention allowthese tasks to be distributed among multiple machines. Hence, acomputationally limited device, such as a smartphone, can provide itsusers with an AR experience by offloading the computationally costlyfeature-to-annotation mapping task to a remote server. Moreover, thesystem provides dynamic multi-user support, in which an expert userviews the scene at a local client, defines objects in the scenedynamically, with these objects being tracked frame by frame anddisplayed to other users, such as a user of a remote client. Thisability allows the local expert (or experts) to provide operationinstructions (in the form of annotations) to a remote novice user. Forexample, in the setting of remote assisted servicing, the remote noviceuser streams live video scenes to the expert user and the AR server viaa video reflector. The expert user annotates the video frames, with theannotations providing detailed service instructions, and sends theannotations to the AR server. The AR server attaches the annotations tofeatures in the videos, and sends the annotation-feature attachmentresult to the remote novice user. The annotation-feature attachmentresult enables the annotations to be correctly aligned with features inthe video (as defined by the expert) and displayed to the remote noviceuser. Therefore, although geographically separated, the remote noviceuser and the local expert can have live annotation-assisted videointeractions.

Computer System

FIG. 6 illustrates an exemplary computer system for distributed objecttracking, in accordance with one embodiment of the present invention. Inone embodiment, a computer and communication system 600 includes aprocessor 602, a memory 604, and a storage device 606. Storage device606 stores a distributed object-tracking application 608, as well asother applications, such as applications 610 and 612. During operation,distributed object-tracking application 608 is loaded from storagedevice 606 into memory 604 and then executed by processor 602. Whileexecuting the program, processor 602 performs the aforementionedfunctions. Computer and communication system 600 is coupled to anoptional display 614, keyboard 616, and pointing device 618.

The data structures and code described in this detailed description aretypically stored on a computer-readable storage medium, which may be anydevice or medium that can store code and/or data for use by a computersystem. The computer-readable storage medium includes, but is notlimited to, volatile memory, non-volatile memory, magnetic and opticalstorage devices such as disk drives, magnetic tape, CDs (compact discs),DVDs (digital versatile discs or digital video discs), or other mediacapable of storing computer-readable media now known or later developed.

The methods and processes described in the detailed description sectioncan be embodied as code and/or data, which can be stored in acomputer-readable storage medium as described above. When a computersystem reads and executes the code and/or data stored on thecomputer-readable storage medium, the computer system performs themethods and processes embodied as data structures and code and storedwithin the computer-readable storage medium.

Furthermore, methods and processes described herein can be included inhardware modules or apparatus. These modules or apparatus may include,but are not limited to, an application-specific integrated circuit(ASIC) chip, a field-programmable gate array (FPGA), a dedicated orshared processor that executes a particular software module or a pieceof code at a particular time, and/or other programmable-logic devicesnow known or later developed. When the hardware modules or apparatus areactivated, they perform the methods and processes included within them.

The foregoing descriptions of various embodiments have been presentedonly for purposes of illustration and description. They are not intendedto be exhaustive or to limit the present invention to the formsdisclosed. Accordingly, many modifications and variations will beapparent to practitioners skilled in the art. Additionally, the abovedisclosure is not intended to limit the present invention.

What is claimed is:
 1. A computer-executable method for tracking anddistributing annotations for a video stream, the method comprising:receiving, at an annotation server, the video stream originating from aremote field computer; extracting a number of features from the receivedvideo stream; identifying a group of features that matches a knownfeature group, wherein the known feature group is associated with anannotation; associating the identified group of features with theannotation; and forwarding the identified group of features and theannotation to the remote field computer, thereby facilitating the remotefield computer to associate the annotation with a group of locallyextracted features and display the video stream with the annotationplaced in a location based at least on locations of the locallyextracted features.
 2. The method of claim 1, further comprising:receiving, from a client computer, a video frame extracted from thevideo stream, wherein the video frame includes annotations added by auser of the client computer; and attaching the annotations tocorresponding features within the video frame based on one or moreuser-definable rules associated with the annotations.
 3. The method ofclaim 2, further comprising: receiving, from the user, updates to theannotations; and reattaching the updated annotations to features withinthe video frame.
 4. The method of claim 2, further comprising:receiving, from the user, updates to the user-definable rules; andreattaching the annotations to features within the video frame based onthe updated user-definable rules.
 5. The method of claim 2, furthercomprising storing the corresponding features and the attachedannotations as known features in a database.
 6. The method of claim 1,further comprising tracking the identified group of features frame byframe in the received video stream.
 7. The method of claim 1, whereinreceiving the video stream involves a video reflector, which reflectsthe video stream originating from a remote field computer to theannotation server and at least one other client computer.
 8. Acomputer-readable storage medium storing instructions that when executedby a computer cause the computer to perform a method for tracking anddistributing annotations for a video stream, the method comprising:receiving, at an annotation server, the video stream originating from aremote field computer; extracting a number of features from the receivedvideo stream; identifying a group of features that matches a knownfeature group, wherein the known feature group is associated with anannotation; associating the identified group of features with theannotation; and forwarding the identified group of features and theannotation to the remote field computer, thereby facilitating the remotefield computer to associate the annotation with a group of locallyextracted features and display the video stream with the annotationplaced in a location based at least on locations of the locallyextracted features.
 9. The computer-readable storage medium of claim 8,wherein the method further comprises: receiving, from a client computer,a video frame extracted from the video stream, wherein the video frameincludes annotations added by a user of the client computer; andattaching the annotations to corresponding features within the videoframe based on one or more user-definable rules associated with theannotations.
 10. The computer-readable storage medium of claim 9,wherein the method further comprises: receiving, from the user, updatesto the annotations; and reattaching the updated annotations to featureswithin the video frame.
 11. The computer-readable storage medium ofclaim 9, wherein the method further comprises: receiving, from the user,updates to the user-definable rules; and reattaching the annotations tofeatures within the video frame based on the updated user-definablerules.
 12. The computer-readable storage medium of claim 9, wherein themethod further comprises storing the corresponding features and theattached annotations as known features in a database.
 13. Thecomputer-readable storage medium of claim 8, wherein the method furthercomprises tracking the identified group of features frame by frame inthe received video stream.
 14. The computer-readable storage medium ofclaim 8, wherein receiving the video stream involves a video reflector,which reflects the video stream originating from a remote field computerto the annotation server and at least one other client computer.
 15. Anannotation server, comprising: a video-receiving mechanism configured toreceive a video stream originating from a remote field computer; afeature-extraction mechanism configured to extract a number of featuresfrom the received video stream; a feature-identification mechanismconfigured to identify a group of features that matches a known featuregroup, wherein the known feature group is associated with an annotation;an annotation-association mechanism configured to associate theidentified group of features with the annotation; and a forwardingmechanism configured to forward the identified group of features and theannotation to the remote field computer, thereby facilitating the remotefield computer to associate the annotation with a group of locallyextracted features and display the video stream with the annotationplaced in a location based at least on locations of the locallyextracted features.
 16. The annotation server of claim 15, furthercomprising: an annotation-receiving mechanism configured to receive,from a client computer, a video frame extracted from the video stream,wherein the video frame includes annotations added by a user of theclient computer; and a rule-based annotation-attachment mechanismconfigured to attach the annotations to corresponding features withinthe video frame based on one or more user-definable rules associatedwith the annotations.
 17. The annotation server of claim 16, wherein theannotation-receiving mechanism is further configured to receive, fromthe user, updates to the annotations, and wherein the rule-basedannotation-attachment mechanism is further configured to reattach theupdated annotations to features within the video frame.
 18. Theannotation server of claim 16, wherein the rule-basedannotation-attachment mechanism is further configured to: receive, fromthe user, updates to the user-definable rules; and reattach theannotations to features within the video frame based on the updateduser-definable rules.
 19. The annotation server of claim 16, furthercomprising a database configured to store the corresponding features andthe attached annotations as known features in a database.
 20. Theannotation server of claim 15, further comprising a feature-trackingmechanism configured to track the identified group of features frame byframe in the received video stream.
 21. The annotation server of claim15, wherein the video-receiving mechanism receives the video stream froma video reflector, which reflects the video stream originating from aremote field computer to the annotation server and at least one otherclient computer.